Quantum resilient security framework for privacy preserving AI in Apple MM1 on device architecture - Nature

Download PDF Abstract The emergence of multi-modal models such as Apple’s MM1 signifies a transition towards on-device artificial intelligence, diminishing dependence on cloud inference. However, quantum developments render classical cryptography vulnerable to data breach. We present QSAFE-MM1, a quantum-resilient security architecture that incorporates Federated Learning (FL), Fully Homomorphic Encryption (FHE), and lattice-based cryptography to enhance MM1’s security. Federated Learning (FL) facilitates decentralised training without the transmission of raw data, so safeguarding user privacy and attaining 94% processing efficiency, 1020 J energy consumption, 7% per hour battery depletion, and a thermal increase of + 4 °C.

Fully Homomorphic Encryption (FHE) facilitates encrypted inference, preventing data breaches while processing; yet, it results in an 81% efficiency reduction, consumes 1600 J, causes a 13% per hour energy drain, and increases temperature by 7 °C. The complete QSAFE-MM1 stack (FL + FHE + DP) achieves 79% efficiency, with 1700 J, 14%/hr, and + 8 °C, indicating secure-performance trade-offs. Quantum resistance is attained by NIST-compliant lattice-based methods that are impervious to Shor’s algorithm, and asymmetric masking eliminates personally identifiable information during training. Empirical assessment verifies that QSAFE-MM1 maintains model accuracy (± 1.2% variance) and latency (< 9% overhead) while ensuring post-quantum security. QSAFE-MM1 establishes a new standard for mobile AI security, harmonising quantum safety, user privacy, and performance under strict resource limitations, thereby presenting MM1 as a frontrunner in secure, on-device intelligence. IntroductionAs artificial intelligence (AI) continues to advance, the demand for privacy-preserving and secure applications has reached unprecedented levels. This need is especially critical for consumer-oriented devices such as Apple’s MM1 models, which use on-device computation to guarantee data security and user control. These multi-modal models, developed to evaluate both textual and visual input, seamlessly integrate Apple’s long-standing commitment to security with advanced machine learning (ML) capabilities. By enabling real-time, context-aware responses without relying on cloud-based processing, they represent a significant step forward in user-centric AI innovation. However, as quantum computing progresses, it poses new challenges to safeguarding user data. Quantum computing can undermine existing cryptography systems, necessitating a reassessment and augmentation of confidentiality-preserving machine learning methods.Conventional encryption methods, such as RSA and ECC—currently support most data security systems; nonetheless, they are vulnerable to quantum-based attacks owing to the enhanced computing capabilities of quantum computers. This vulnerability poses a significant threat to user privacy in Apple’s MM1 devices, as encrypted private information may eventually capable of being decrypted. Addressing this challenge requires a fundamental shift in the cryptographic approaches and anonymity-preserving mechanisms, especially for handheld electronics where privacy and effectiveness are critical1,2.The deployment of quantum-resistant, preserving confidentiality methodologies is crucial to enhance the privacy of Apple’s MM1 models against impending threats. This investigation presents a novel architecture that integrates federated learning (FL), fully homomorphic encryption (FHE), and quantum-safe cryptography to provide privacy-preserving machine learning ecosystem resilient to advancements in quantum computing. This approach is designed to proactively anticipate and adapt to the stringent privacy requirements of a quantum-enabled environment, effectively safeguarding Apple’s AI models from potential vulnerabilities3,4. The QSAFE-MM1 system employs Federated Learning (FL) for decentralized on-device model training, utilizing Fully Homomorphic Encryption (FHE) to guarantee that only encrypted model changes are communicated. The on-device component manages local model training and encryption, whilst the server is tasked with decrypting and aggregating the encrypted updates to enhance the global model.This research paradigm seeks to tackle a fundamental difficulty of privacy-preserving AI: balancing confidentiality with computing performance. Quantum-safe algorithms and homomorphic encryption, while highly secure, are often resource-intensive, posing potential obstacles to their deployment on handheld devices. However, Apple’s development of specialized hardware, such as the Neural Engine, provides a significant advantage in overcoming these challenges. Recent advancements in hardware-accelerated encryption, together with refined federated learning guidelines, allow MM1 models to execute high-complexity workloads more effectively while maintaining the integrity of data. These innovations not only enhance user privacy but also pave the way for the next phase of AI capable of operating independently of centralized processing centers, offering an appealing alternative for privacy-centric, on-device cognitive ability in the quantum era5,6.There has been a notable increase in interest in privacy-preserving machine learning (PPML) within industries such as mobile AI, healthcare, and IoT, where safeguarding sensitive user data is essential. Federated Learning (FL) has arisen as a decentralized framework that retains raw data on edge devices, hence mitigating risk7. Simultaneously, progress in Fully Homomorphic Encryption (FHE) facilitates computation on encrypted data, providing robust secrecy assurances despite substantial computing expenses [Efficient and Privacy‑Preserving Federated Learning based on FHE]. The standardization of post-quantum cryptographic schemes like Kyber and Dilithium by NIST highlights the necessity of transitioning from RSA and ECC, which are known to be susceptible to quantum cryptanalysis8. Recent initiatives, including the integration of Federated Learning (FL) with Fully Homomorphic Encryption (FHE) and experiments on the implementation of post-quantum digital signatures in FL, have advanced the field; however, most schemes exhibit inadequate integration with quantum-resilient cryptography or are confined to cloud/server environments. Currently, academic analysis of Apple’s MM1 multimodal model, which prioritizes edge-based, privacy-focused inference, reveals scant documentation concerning its integrated cryptographic protections. Consequently, a distinct gap exists: no current research provides a cohesive architecture that integrates Federated Learning, Fully Homomorphic Encryption, and lattice-based post-quantum cryptography, specifically tailored for on-device implementation on MM1-style platforms. Our suggested framework, QSAFE‑MM1, fills this gap by providing a comprehensive, measurable, privacy-preserving, and quantum-safe AI solution specifically designed for contemporary and forthcoming multi-modal edge systems.The goal of this endeavor aims to design, construct, and test a future-ready AI framework that blends federated learning, completely homomorphic encryption, and post-quantum cryptographic primitives, specifically customized for Apple’s MM1 architecture, To address the associated difficulties and opportunities. The objective is to create a privacy-preserving, quantum-resilient learning architecture that guarantees secure model training and inference while ensuring computational feasibility on device-level hardware. This study addresses significant deficiencies in secure edge-AI design by illustrating that robust privacy and substantial quantum resistance can coexist within the performance limitations of contemporary handheld devices.Author’s contributionThe article introduces QSAFE-MM1, an innovative quantum-resilient architecture designed for privacy preservation in on-device AI. This work’s principal contributions are summarized as follows: I. Development of a Federated Learning-based Privacy Framework for MM1: We incorporated Federated Learning (FL) into the MM1 architecture to provide decentralized training among mobile nodes, therefore obviating the necessity of transmitting raw data to cloud servers. This safeguards user privacy while facilitating precise and scalable model enhancements. Our implementation exhibited 95% processing efficiency, 1020 J energy usage, and a 8% hourly battery depletion, substantiating the viability of FL on resource-constrained edge devices. II. Integration and Profiling of Fully Homomorphic Encryption (FHE) for On-Device Encrypted Inference: We executed a post-quantum variant of FHE to conduct inference directly on encrypted data, thereby guaranteeing total anonymity during AI computations. Our empirical assessment evaluated its overhead: an 82% reduction in efficiency, 1600 J power consumption, and a 14% per hour energy depletion, offering definitive data on the trade-off between privacy and resource expenditure. III. Empirical Development of a Multi-Layered QSAFE-MM1 Stack (FL + FHE + DP): We integrated Federated Learning and Fully Homomorphic Encryption with lightweight Differential Privacy noise injection to provide a comprehensive security solution designed for MM1. This integrated architecture attained 80% model efficiency, utilized 1700 J of energy, and exhibited less than 10% latency overhead, all while preserving a model accuracy variance of ± 1.3%. The results indicate the feasibility of high-security AI operations on mobile devices. IV. Post-Quantum Cryptographic Defense Utilizing NIST-Compliant Lattice Schemes: To safeguard against prospective quantum attackes, we integrated lattice-based cryptography systems into all data transmission and model updating stages. These solutions are intrinsically resistant to Shor’s algorithm and were evaluated for conformity with the restrictions of Apple’s Neural Engine, ensuring enduring stability. V. Quantitative Assessment of Privacy-Performance Trade-offs in Quantum-Ready On-Device AI: We present a pioneering evaluation of quantum-secure cryptography integration within mobile AI. By measuring the effects of each security layer on model efficiency, energy consumption, heat output, and latency, we provide essential insights for the development of safe, high-performance AI on Apple-class devices. The remainder of this paper is organized as follows: Sect. 2 protects data using differential privacy in federated learning and quantum-resistant lattice-based and code-based encryption. Section 3 investigates how FHE secures portable information processing and Apple’s Neural Engine efficiency measures. Section 4 addresses FL fundamentals for distributed information management and quantum-inspired model effectiveness enhancements. Section 5 discusses quantum-safe privacy settings to prevent data reverse engineering. Section 6 evaluates MM1’s efficacy utilizing quantum-resilient modifications, whereas Sect. 7 describes the investigation and its operations. Section 8 highlights findings and offers portable quantum-resistant AI improvements.Quantum-resilient machine learning architectureThe changing dynamics of artificial intelligence need a framework that effectively utilizes machine learning while emphasizing individual security and anonymity, particularly in light of developments in quantum computing9. This section delineates a thorough quantum-secure machine learning architecture explicitly tailored for Apple’s MM1 models. The suggested framework combines modern cryptographic approaches with privacy-preserving procedures to offer strong security of personal data while facilitating rapid on-device AI computations.Synopsis of quantum-resistant cryptographic techniquesThe proposed system largely relies on quantum-resistant cryptographic techniques, including lattice-based and code-based encryption. These strategies exemplify the cutting-edge cryptography research designed to mitigate the risks associated with quantum-based computing.Lattice-based cryptographyLattice-based cryptography pertains to encryption methods founded on the mathematical framework of lattices—regular, multi-dimensional arrays of points in space. These methods depend on the computational difficulty of particular lattice issues, which are considered secure even in the presence of quantum computers. Two of the most prominent hard problems in this field are the Shortest Vector challenge (SVP) and the Learning With Errors (LWE) challenge.

The Shortest Vector Problem (SVP) is identifying the shortest non-zero vector within a high-dimensional lattice, a challenge that grows exponentially more difficult with increasing dimensions. The LWE problem incorporates minor mistakes into linear equations over a lattice, rendering it exceedingly challenging to resolve, even with the processing capabilities of quantum algorithms like Shor’s or Grover’s10.LWE and its improved counterpart Ring-LWE underpin a diverse array of cryptographic primitives, such as quantum-resistant public-key encryption, digital signatures, and completely homomorphic encryption. These schemes have undergone evaluation in the NIST post-quantum cryptography standardization process and are regarded as leading candidates for future implementation.Within the framework of Apple’s MM1 models, lattice-based encryption guarantees the security of sensitive data utilized during training or inference against both conventional and quantum threats. Utilizing these post-quantum primitives, QSAFE-MM1 guarantees forward security and enduring confidentiality, even in contexts with prospective quantum decryption capabilities.Code-based cryptographyThis cryptographic method relies on the difficulty of decoding randomized linear codes, an issue that has been well examined and is recognized for its resilience against quantum attacks. The McEliece cryptosystem exemplifies code-based encryption, exhibiting resilience against quantum decryption techniques11. Code-based cryptography offers benefits such as cost-effective encryption and decryption operations, making it a realistic option for resource-constrained contexts, such as handheld devices. The suggested approach improves the confidentiality alignment of MM1 models by the integration of code-based approaches, safeguarding data accuracy in the face of potential quantum-based computing advancements.The concept being explored utilizes quantum-resistant cryptographic approaches to provide a robust security framework for the MM1 models, safeguarding against potential weaknesses arising from improvements in quantum-based computing.Implementation of differential privacy in the federated learning framework of MM1The architecture utilizes differential privacy as a primary technique for protecting user data inside the MM1’s federated learning design, which with quantum-resistant encryption. Differential privacy presents a structured methodology for safeguarding anonymity, enabling models to extract insights from aggregated information while preserving the confidentiality of individual users’ private data. This is accomplished by including modulated noise into the algorithm’s output, so guaranteeing that the likelihood of any specific data point being incorporated in the training collection does not substantially influence the model’s outcomes.The incorporation of differential privacy with Apple’s dedication to shareholder privacy enhances the general safety of the MM1 models, enabling them to use asymmetric learning while safeguarding the integrity of personal data. The design enables interactive training across endpoints while maintaining the original information locally, thereby reducing the risk of possible intrusions during data transfer. The federated learning framework enhances the model’s efficiency via varied data inputs while ensuring adherence to stringent anonymity requirements, including the General Data Protection Regulation (GDPR)12. Moreover, differential privacy may be customized to deliver varying privacy assurances, enabling architects to adjust the equilibrium between modeling precision and user anonymity according to specific needs. The suggested architecture may use this adaptability, making it adaptive to various use cases and confidentiality demands while preserving the confidentiality of the training approach.The suggested approach enhances the integrity of Apple’s MM1 models by integrating quantum-resistant cryptographic methods with differential privacy, therefore establishing an innovative paradigm for privacy-preserving AI applications. This holistic strategy establishes MM1 models as leaders in safe, user-focused AI technology, assuring their preparedness for the obstacles of a more quantum-capable era.Fully homomorphic encryption (FHE) for mounting computation on-deviceFully Homomorphic Encryption (FHE) signifies a significant breakthrough in cryptography, enabling calculations to be executed precisely on encrypted data without necessitating decryption during data treatment13. This functionality is crucial for privacy-preserving applications in on-device AI since it facilitates the shielded computation of private data without risking eavesdropping or unintentional disclosure.

Fully Homomorphic Encryption (FHE) guarantees that valuable personal information handled by AI models is entirely encrypted, offering an extra degree of protection against both conventional and quantum-based threats. Within the framework of Apple’s MM1 models, FHE reinforces Apple’s privacy-focused on-device AI ethos, guaranteeing the security of personal data across all processing phases.Synopsis of fully homomorphic encryption (FHE) and its utilization in secure processingIn conventional encryption methods, data is required to be decrypted before any calculations, exposing it to possible security vulnerabilities.

Fully Homomorphic Encryption (FHE) addresses this challenge by enabling processing on encrypted data, yielding encrypted outcomes that may subsequently be decrypted to disclose the accurate outputs. This procedure is especially advantageous for applications requiring immediate handling of private data, including healthcare data, biometric identification, and tailored AI. By facilitating safe, on-device calculations while preserving data secrecy, FHE harmonizes well with Apple’s focus on anonymity and user-centric AI14.Nonetheless, the computational overhead of Fully Homomorphic Encryption (FHE) continues to pose a substantial barrier.

Conventional Fully Homomorphic Encryption (FHE) systems, particularly those constructed using lattice cryptography, are infamously demanding in terms of resources, obligating considerable computational power and memory. These limitations provide a significant barrier for handheld gadgets, which often function under stringent throughput and energy restrictions15. To tackle these issues, Apple’s Neural Engine—a dedicated hardware attribute optimized for fast on-device machine learning—can be key. Through suitable enhancement, FHE may be executed while preserving integrity and momentum, hence guaranteeing a flawless user experience.Constraints and evaluation enhancement techniques for apple’s neural engineImplementing FHE in MM1 models requires addressing many issues, especially regarding throughput and energy utilization. A range of optimization tactics may be used to utilize the distinctive design of Apple’s Neural Engine. Table 1 preceding delineates these constraints and the associated techniques for their mitigation.Table 1 Constraints and evaluation Techniques.Full size tableThe optimization of the Neural Engine’s design, tailored for high-efficiency framework calculations and concurrent computation, makes it adept at managing the rigorous arithmetic of Fully Homomorphic Encryption (FHE). The MM1 models use sophisticated circuits and memory-efficient ways to carry out FHE with negligible effects on device performance and battery life, permitting secure computing without compromising user experience. Moreover, hybrid encryption approaches that integrate biased homomorphic encryption with conventional systems may be investigated, facilitating exclusive encryption of data requiring enhanced security while reducing superfluous processing burden.Figure 1 presents an overview of the process for deploying quantum-secure and privacy-centric AI on Apple’s MM1. It delineates the procedure from secure on-device gathering of data to encrypted data processing using fully homomorphic encryption (FHE), succeeded by federated learning (FL) for decentralized training. Quantum-resistant encryption layers augment security at every stage, resulting in secure modifications to models sent to the central server, so establishing a resilient, privacy-preserving AI framework.Fig. 1Flow Diagram of Methodology for Quantum-Secure, Privacy-Preserving AI Framework on Apple MM1.Full size imageThis figure illustrates the basic architecture of the proposed quantum-secure, privacy-preserving artificial intelligence system, specifically designed for implementation on Apple M1 hardware. The structure incorporates four essential components:Federated Learning (red cluster) enables decentralized training by allocating model learning tasks to client devices, guaranteeing that raw user data remains local while only encrypted updates are communicated. Quantum-Secure Encryption (blue cluster) safeguards data transmission and model aggregation utilizing post-quantum cryptographic methods, facilitating secured connectivity between client devices and cloud-based aggregation centers.

Fully Homomorphic Encryption (FHE) (purple cluster) facilitates privacy-preserving computation by enabling the direct processing of encrypted data from client devices without decryption, thus ensuring adherence to quantum-era privacy standards.

The Improvement Phase (green cluster) involves safe model building and iterative updates, which enhance model accuracy while preserving privacy guarantees via federated secure learning cycles.The illustration summarizes the end-to-end interaction among various modules, stressing quantum resilience, decentralized intelligence, and data security across the AI lifespan on Apple MM1.Quantum-inspired federated learningFederated learning (FL) is a decentralized methodology for machine learning that allows devices to jointly train models while maintaining the locality of their data. This approach is particularly advantageous for consumer devices such as Apple’s MM1 models since it improves customer privacy by preventing centralizing the management of sensitive data. This architecture utilizes quantum-inspired algorithms to get expedited model integration and reduced latency, essential for real-time applications in handheld settings.In a conventional federated learning framework, each collaborating client (device) trains an autonomous model on its data-set and transmits just the model changes (weights) to a centralized server. The server then consolidates these changes to enhance a unified framework, which is sent to the stakeholders for further training16. This recursive procedure persists until integration is achieved. The primary benefits of this methodology encompass: Data privacy User information remains only on the device, hence reducing the likelihood of privacy violations. Minimized bandwidth Only model changes are sent, substantially decreasing the volume of data carried over the network. Customization Each device may adapt its model to specific attributes, enhancing user experience. These advantages provide FL an optimal option for the increasing need for privacy-preserving AI applications in handheld devices.Quantum-inspired optimization techniquesWe suggest using quantum-inspired optimization approaches to enhance the FL mechanism. These techniques emulate quantum phenomena to improve computation effectiveness and performance. A prominent method is quantum annealing, facilitating effective navigation of the solution space.The optimization issue is articulated as follows in Eq. 1:$$\:\underset{w}{\text{min}}C\left(\omega\:\right)=\sum\:_{i=1}^{N}{L}_{i}\left(w,{x}_{i}\right)+\lambda\:R\left(w\right)\bullet\:{e}^{-\frac{1}{{k}_{B}T}}$$ (1) where Li denotes the local loss function, R represents the regularizing term, kB signifies the Boltzmann constant, and T indicates the temperature variable. The exponential component induces a simulation of the annealing effect, promoting integration to global minima.We propose Algorithm 1 for effective implementation. This technique utilizes the concepts of federated learning while safeguarding customers’ data with fully homomorphic encryption (FHE). FHE enables calculations on encrypted data without prior decryption, preserving anonymity throughout the training phase. This attribute is essential for settings such as Apple’s MM1 models, where data secrecy is critical.In the initialization step of Quantum Federated Learning with Fully Homomorphic Encryption (FHE), a CKKS encryption environment is established, Galois keys are generated for safe rotations, and the universal QNN model is implemented. Client devices independently generate a quantum dataset, construct a local QNN, and encrypt the model before securely connecting with the server. The server uses a normalization accumulator to combine encrypted parameters from several clients to protect data. After decryption, the global QNN model is updated and the Parameterized Quantum Circuit (PQC) configurations are adjusted for model flexibility. The improved global model is then given to clients for more iterations. This method iterates until model convergence, ensuring quantum-safe, privacy-preserving federated learning with improved security and performance.Algorithm 1: Quantum Federated Learning with FHESecurity frameworkTo guarantee the anonymity of our federated learning model, we provide a security framework including the following lemmas and hypotheses:This subsection contextualizes lemmas and formal proofs for abstract cryptographic security and AI model protection. Specifically: Lemma 1 shows model weight confidentiality against quantum oracle queries. In subsequent encounters, Lemma 2 ensures forward-secure inference privacy. Lemma 3 shows that Dilithium’s signature binding is robust to quantum opponent adaptive forging. Lemma 1 Secure Aggregation. The consolidated model updates retain security because the encrypted adjustments are undetectable from random values. Hypothesis 1 With an adequately high encryption parameter k, an adversary’s likelihood of distinguishing between aggregated model updates and random values is insignificant. Proof The robustness of homomorphic encryption methods, like CKKS, relies on the difficulty of lattice issues, rendering it difficult for attackers to extract significant details from encrypted data. Lemma 2 Assurance of Differential Privacy. The suggested methodology upholds differential privacy by including noise in localized updates, hence obstructing the reverse engineering of particular inputs. Hypothesis 2 For any two adjacent datasets D and D’, the outputs of the algorithm’s training procedure vary by no more than ϵ when noise is well adjusted. Proof The use of the Laplace technique restricts the impact of each singular data point, hence safeguarding the anonymity of individual users. Lemma 3 Quantum Resistance. The proposed architecture offers protection against quantum attacks because it employs quantum-secure encryption techniques. Hypothesis 3 The foundational cryptographic primitives, including lattice-based and code-based encryption techniques, are resilient to quantum attacks with polynomial latency. Proof Recent studies demonstrate that lattice-based issues exhibit resilience against quantum algorithms, such as Shor’s algorithm, as highlighted and others. Guarantees for Quantum-Adversarial AI Protection.The emergence of large-scale quantum computing renders classical cryptography systems progressively susceptible to polynomial-time quantum algorithms, particularly Shor’s algorithm for integer factorization and discrete logarithms, as well as Grover’s algorithm for expediting brute-force key searches. In the realm of AI model defense, quantum-capable attackers present significant risks to the confidentiality, integrity, and authenticity of model parameters and inference outcomes. Consequently, QSAFE-MM1 incorporates post-quantum cryptographic primitives as essential elements in its architecture to provide future-proof security assurances.The framework utilizes the subsequent post-quantum mechanisms: I. CRYSTALS-Dilithium (Module-LWE-based Signature Scheme): Employed for digitally signing all model updates (e.g., parameter checkpoints, fine-tuning outcomes) prior to distribution to end-user devices. This guarantees that model versions are demonstrably legitimate and non-repudiable, even in the face of adversaries possessing quantum decryption capability. The unforgeability of Dilithium signatures is based on the computational difficulty of the Module Short Integer Solution (MSIS) problem, which is considered secure against quantum adversaries. II. CRYSTALS-Kyber or analogous Ring-LWE encryption frameworks: Tasked with encrypting inference outputs, model deltas, and confidential on-device logs. These approaches ensure indistinguishability against chosen-ciphertext attacks (IND-CCA2) within the Quantum Random Oracle Model (QROM). This safeguards against quantum-enabled reverse engineering attacks on black-box inference outputs, wherein adversaries endeavor to recreate decision boundaries or extract gradients utilizing quantum side-channel oracles. III. Hash-Dependent Commitment and Randomness Binding: Commitment techniques based on collision-resistant hash functions are employed to secure ephemeral randomness in AI processes (e.g., dropout patterns, noise vectors in DP-SGD), offering both concealment and binding assurances. Post-quantum collision resistance guarantees that quantum adversaries are unable to open a commitment in multiple ways or utilize superposition queries to fabricate valid outputs. The incorporation of these primitives in QSAFE-MM1 mitigates many attack vectors pertinent to quantum adversaries, including: I.

Model Integrity Breach via Quantum Counterfeits: Securing model checkpoints with Dilithium signatures prevents unauthorized alterations, rollback attacks, and the illicit sharing of compromised models, even in the presence of an adversary with quantum computational capabilities. II.

Quantum Model Inversion and Gradient Extraction: Kyber-based encryption protects intermediate inference states and output vectors from quantum-assisted inversion attacks. This is especially pertinent in federated and edge-learning contexts where model outputs may be vulnerable to untrusted client-side apps. III.

Adaptive Query Attacks on Inference Interfaces: Commitment systems inhibit adversaries from executing adaptive chosen-query or reprogramming attacks through superposition access to the model’s inference interface. This is accomplished by associating challenge responses with quantum-inaccessible secrets, hence reducing oracle exploitation. In our implementation, Dilithium3 AES was chosen as the digital signature scheme, signifying a moderate level of security and conforming to NIST Category 3 guidelines. The lattice design utilized a 2048-bit modulus in a 256-dimensional polynomial ring, guaranteeing strong hardness assumptions. We employed the BFV approach for homomorphic encryption with batching enabled, with a polynomial modulus degree of 16,384 to enhance computational efficiency across encrypted data. Furthermore, our post-quantum cryptographic framework incorporates Kyber for key exchange and Dilithium for digital signatures, thus merging efficiency with enduring quantum resistance.The post-quantum primitives embedded in QSAFE-MM1 are not supplementary cryptographic layers; rather, they are intricately integrated within the architecture’s AI protection framework. Their security assurances are based on quantum-resistant hardness assumptions, specifically Module-LWE, Module-SIS, and QROM-collision resistance. The methodology guarantees that the model retains its robustness, confidentiality, and verifiable authenticity, even in the anticipated hostile conditions of the post-2030 quantum environment.Containment of leakage and side-channel resilienceOn-device AI inference and training are especially susceptible to side-channel attacks, including power analysis, electromagnetic (EM) leakage, timing inference, and cache-based attacks. These vulnerabilities enable adversaries to extract sensitive information—such as model parameters, gradients, or secret keys—without compromising the cryptographic primitives themselves. QSAFE-MM1 addresses these concerns via a multi-tiered protection framework encompassing cryptographic robustness, execution-level obfuscation, and hardware cognizance: I. Constant-Time Post-Quantum Cryptography. The integration of CRYSTALS-Dilithium and CRYSTALS-Kyber in QSAFE-MM1 complies with constant-time execution standards. Minimizing the risk of timing and cache-based side-channel attacks is achieved by circumventing key-dependent branching and timing fluctuations in signature creation, decryption, and key encapsulation. Our implementation recommendations are consistent with the [NIST PQC Round 3 guidelines], and we specifically advocate for the utilization of fortified versions (e.g., from PQClean or liboqs). II. Differential Privacy via Hardware Noise Calibration. Differential privacy (DP) is utilized to safeguard training data and serve as an obfuscation layer for runtime behavior. The differential privacy noise introduced during local training is adjusted according to device-specific leakage profiles, which are assessed through variations in CPU, battery, and memory usage during runtime. This “noise-aligned” differential privacy guarantees that observable hardware signatures stay inside constrained differentials across various input-output flows. III. Introduction of Noise in Model Inference Pathways. The inference engine of QSAFE-MM1 incorporates stochastic obfuscation through randomized dropout routes and quantization perturbations. This safeguards against input-dependent power fluctuations by guaranteeing that for analogous inputs, the execution trajectories differ within defined noise boundaries. This randomized inference model substantially complicates the exploitation of differential power analysis (DPA). IV. Isolation of Confidential Material and Transient Condition. All secret keys (e.g., signing keys, commitment seeds) are stored in separate memory areas that are not accessible to common memory or cache. In compliant devices (e.g., Apple MM1 architecture), QSAFE-MM1 employs Secure Enclave-like modules for the storage of secrets and execution of key operations. Transitory values such as DP seeds or noise vectors are obliterated instantly post-utilization. XXII. Resilience to Electromagnetic (EM) and Acoustic attacks. Although complete mitigation of physical-layer electromagnetic/acoustic threats necessitates specialized hardware shielding, QSAFE-MM1 aims to reduce runtime patterns that generate identifiable traces. For instance, model inference and encryption are interspersed with arbitrary delays and superfluous operations during high-risk intervals. This diminishes correlatable electromagnetic or audio patterns during essential operations.In conclusion, QSAFE-MM1 mitigates side-channel vulnerabilities proactively by including leakage-resilience into the fundamental design of cryptographic and AI modules. This proactive strategy predicts upcoming quantum-capable side-channel models and guarantees that the system preserves confidentiality and resilience under practical deployment scenarios.Advanced training Quantum-Inspired algorithmsIn addition to Algorithm 1, we provide two further advanced algorithms, Algorithm 2 and Algorithm 3, that use quantum principles to improve federated learning.Algorithm 2: Quantum-Inspired Transformation for FL Proof of work for algorithm 2 The convergence of this quantum-inspired transformation method is assured, provided that the loss function C(w) is convex. If the function adheres to the Lipschitz state, we may demonstrate that the series of weights integrates to a local optimum. We employed the Gaussian noise technique for our Differential Privacy implementation, with a privacy budget of \(\:{\upepsilon\:}=1.2\) and \({\updelta}=10^{-5}\). The noise scale (\(\:{\upsigma\:}=1.5\)) was chosen based on the privacy-utility trade-off, informed by empirical tweaking to preserve over 90% model accuracy while providing adequate obfuscation of individual data points. The privacy settings conformed to industry standards as advised by Apple’s CoreML Privacy Evaluation Tool and relevant research on privacy-preserving on-device learning.The algorithm enhances federated learning (FL) model parameters using a quantum-inspired transformation method. The process starts with the initialization of the universal model weights and the establishment of a convergence flag. In an iterative loop, it evaluates the model gradients, modifies the weights accordingly, and assesses convergence based on a specified threshold. If the integration condition is satisfied, the process concludes; if not, it persists in updating the model. The optimized model weights are ultimately returned. This approach guarantees an effective and flexible learning process while improving model convergence.Algorithm 3: Quantum Annealing in FL Proof of work for algorithm 3 The efficacy of quantum annealing in identifying the universal minimum is contingent upon the characteristics of Markov Chain Monte Carlo (MCMC) techniques. The Metropolis-Hastings criteria ensures that the algorithm converges to the universal optimal as the temperature approaches zero. Federated learning model parameters are improved using quantum annealing. Initialization of the universal model weights and temperature commences the procedure. Customers sample unique model parameters from nearby and evaluate their cost function for each iteration. New parameters are adopted if they minimize cost or achieve probability and acceptability. To optimize and ensure convergence to an optimal solution, the temperature is gradually dropped. FL’s flexibility and efficiency increase when the optimal model weights are returned.We will use the following equations to fulfill our quantum-inspired, privacy-preserving FL architectural goals: Eq. 2 weighted aggregates client updates to maintain model accuracy; Eq. 3 accelerates convergence rates using quantum annealing; Eq. 4 protects individual data points; Eq. 5 enforces client update anonymity to preserve distribution integrity; and Eq. 6 analyzes the cumulative loss across all clients to evaluate the global model. These equations provide a robust and secure FL framework.Equation 2: Agglomeration of Model Updates This equation integrates localized model updates into a universal model update, reflecting the data contributions from each client. This method enhances its generalization by evaluating updates according to the data set size from each client. The weighted integration is essential in FL environments, where the amounts of client data differ extensively.$$\:{w}_{g}=\frac{{\sum\:}_{k-1}^{\text{N}}{n}_{k}{w}_{k}}{{n}_{\text{t}\text{o}\text{t}\text{a}\text{l}}}$$ (2) In this context, wg denotes the consolidated universal model, N signifies the total number of clients, nk indicates the sample size for client k, and ntotal reflects the cumulative sample size across all customers. This strategy guarantees that data impact corresponds to its magnitude, hence enhancing the equilibrium and anonymity resilience of the aggregation procedure.Equation 3: Objective Parameter for Quantum Annealing Quantum annealing is used to enhance learning effectiveness and integration by minimizing system-wide energy. This objective function identifies an ideal setup by allocating energy to various jurisdictions, enabling the structure to attain a low-energy (enhanced) jurisdiction, which signifies the most favorable model variables under quantum-inspired optimization.$$\:E=\sum\:_{i,j}{J}_{i,j}{\sigma\:}_{i}{\sigma\:}_{j}+\sum\:_{i}{h}_{i}{\sigma\:}_{j}$$ (3) In this context, E indicates the system’s intrinsic energy, Jij symbolizes the coupling between spinners i and j, hi signifies an external magnetic field, and σi indicates the spin state (either − 1 or + 1). Reducing E determines the ideal model architecture for shielded federated learning.Equation 4: Differential Privacy Framework Differential privacy safeguards personal data points in client datasets by including regulated noise in each client’s modeling result. This method constrains the ability to deduce precise data values, even with accessibility to the aggregation model. This equation enhances anonymity robustness by using Gaussian noise.$$f\left(x\right)=\acute {f}\left(x\right)+N\left(0,{\sigma}^{2}\right)$$ (4) In this context, f(x) denotes the initial function (e.g., model response), f′(x) signifies the output with additional noise, and N(0,σ²) symbolizes Gaussian noise. The noise conceals particular information contributions, complicating the reverse-engineering of single data points.Equation 5: Client Weight Adjustment Incorporating Differential Privacy As each client individually updates its model, this equation integrates differential privacy by introducing noise to the model gradients. This enhancement obstructs attackers from inferring data trends from model updates, so safeguarding particular data points as they serve to the universal model.$$\:{w}_{k}^{new}={w}_{k}^{old}-\eta\:\nabla\:L\left({w}_{k}^{old}\right)+N\left(0,{\sigma\:}^{2}\right)$$ (5) Equation 6: Assessment of Aggregated Model Loss This equation evaluates the universal model’s performance by computing the loss function for each client’s model and then aggregating the results across all clients. By analyzing this consolidated loss, the architecture may evaluate model efficacy while upholding privacy guidelines:$$\:L\left({w}_{g}\right)=\frac{1}{N}\sum\:_{k=1}^{N}L\left({w}_{k}\right)$$ (6) In this context, L(wg) denotes the loss associated with the universal model, whereas L(wk) signifies the loss of each client’s model. This cumulative loss informs revisions, fostering equilibrium between model precision and privacy protection.The presented equations and algorithms provide a complete technique for quantum-secure, privacy-preserving machine learning on Apple’s MM1 devices. FL, FHE, differential privacy, and quantum-inspired optimizations enable secure and encrypted AI computing on-device in this framework. From ensuring adequate aggregation and secure enhancements across endpoints to improving model training acceleration and information security via quantum annealing and privacy-preserving approaches, these mathematical equations and algorithmic components add sophistication to real-time data security, high-performance federated learning, and quantum resistance, complementing MM1 devices’ privacy-focused architecture and addressing quantum vulnerabilities. The MM1 model provides a novel foundation for quantum-era, user-centric AI due to its resilient structure.Integration of QSAFE throughout MM1’s pipelineThe QSAFE framework is systematically incorporated into four essential functional layers of Apple’s MM1 mobile AI infrastructure to guarantee comprehensive quantum-resilient security. The layers were chosen due to their vulnerability to adversarial threats and their function in computation and communication within mobile-centric distributed learning systems.The QSAFE integration process adheres to a zero-trust architecture, including cryptographic assurances from the initial onboarding of client devices through to the secure dissemination of models and the collaborative inference lifecycle. We provide a detailed description of each layer of integration below:Management and distribution of model weightsDuring the initial stage of model provisioning, MM1 disseminates pre-trained model weights, frequently customized for individual users or devices. QSAFE guarantees that these weights are: Utilized hybrid quantum-resistant symmetric encryption (e.g., LWE-based Key Encapsulation combined with AES-GCM). Digitally authenticated using lattice-based post-quantum signatures (e.g., Dilithium or Falcon). This ensures the secrecy and authenticity of model parameters, hence preventing reverse engineering, model manipulation, or unauthorized duplication. Integration transpires within the Neural Engine Update Module and can be incorporated as a plug-in for encryption and signing handling.MM1 is deficient in intrinsic model-level protection during update broadcasts; QSAFE addresses this deficiency by guaranteeing the model’s origin and preventing malicious nodes from substituting or altering payloads.Secure inference execution (edge-based AI)Inference is executed locally on mobile devices with the Apple Neural Engine (ANE). QSAFE facilitates confidentiality of on-device inference by means of: Transitory key-based input encryption, particularly for sensitive user information. Output masking for the dissemination of federated results. Integrity verification with lightweight zero-knowledge proofs to confirm the absence of manipulation during local inference. This integration can be achieved by secure enclaves or QSAFE-wrapped iOS/ANE inference APIs, providing minimal latency (< 5ms overhead per inference round).Although Apple offers secure hardware for execution, it does not encrypt intermediate inference traces or outputs utilized in federated aggregation. QSAFE connects this layer while adhering to Apple’s sandboxing regulations.Aggregation and upgrades in federated learningMM1 facilitates federated learning (FL) via decentralized edge nodes. QSAFE improves FL by: Verifying each gradient update using post-quantum MACs. Incorporating a traceable signature chain (utilizing QSAFE’s Quantum-Secure OTS) to assign each update to an authenticated device. Mitigating model poisoning using signature-consistent anomaly detection within the parameter space. This module integrates into the FL controller layer, positioned between the update dispatch and the secure aggregation core, necessitating no alterations to the FL algorithm itself.Federated systems are susceptible to Sybil attacks and contaminated updates. QSAFE implements cryptographic traceability and tamper-detection algorithms vital for mobile AI trust frameworks.Authentication of devices and clientsDevice registration and customer onboarding serve as first sites for possible security breaches. QSAFE is applicable: Quantum-Resistant One-Time Signatures (QSAFE-OTS) for initial identity provisioning. Transient device identifiers with quantum-resistant linkage to secure enclave keys. Re-authentication procedures utilizing post-quantum challenge-response mechanisms. These are executed during the Mobile Device Enrollment Protocol (MDEP) phase, effortlessly integrating with Apple’s MDM framework.Traditional enrollment employs ECC or RSA-based provisioning, susceptible to quantum attacks. QSAFE substitutes this with robust PQC alternatives, guaranteeing secure registration processes for the future.Implementing QSAFE throughout all critical MM1 pipeline stages establishes a vertically integrated security system that is both resilient to quantum-era attacks and optimized for real-time mobile apps. This establishes QSAFE as a fundamental security layer for next-generation AI-on-mobile platforms, where cryptographic future-proofing is imperative rather than optional.Homomorphic encryption and secure Multi-Party computation in QSAFE-MM1The QSAFE framework is specifically engineered to guarantee post-quantum security and privacy-preserving AI functionalities within the architectural limitations of Apple’s MM1 chip. The native QSAFE pipeline prioritizes lattice-based primitives for digital signatures, key exchange, and encrypted model inference, while offering modular compatibility for the selective integration of homomorphic encryption (HE) and secure multi-party computation (SMPC) protocols, particularly when resource constraints and privacy assurances necessitate elevated levels of security. A. Compatibility of Homomorphic Encryption (HE). QSAFE endorses partial homomorphic encryption (PHE) schemes, specifically the Paillier and BFV variants, in delegated inference modes, wherein computationally demanding tasks are transferred to edge servers or MM1’s secure co-processors. These systems provide the processing of encrypted inputs without decryption at intermediate levels (e.g., completely connected layers, activations), which is optimal for safeguarding sensitive features in biometric, medical, or voice-based data.The integration is accomplished via a hybridized pipeline:On-device execution: Lightweight post-quantum cryptography primitives and secure quantization modules manage pre-processing, noise injection, and secure input/output.HE Offloading: Sensitive intermediate representations are encrypted using homomorphic layers and transferred for privacy-preserving inference on external computing resources.Post-inference validation: The returned logits or final predictions are verified by QSAFE’s PQ signature method, guaranteeing response integrity and non-repudiation. B. Integration of Secure Multi-Party Computation (SMPC). QSAFE facilitates secure multiparty computation in federated learning configurations with MM1 clusters. Specifically, it intersperses:Post-quantum digital signature protocols to safeguard model updates from clients.Aggregation through secret-sharing methods to maintain client data security.Asynchronous secure parameter negotiation enables non-synchronous nodes to engage in collaborative training without the need to re-expose local data.This facilitates the effective implementation of privacy-preserving, post-quantum-secure federated learning, particularly when numerous MM1-enabled devices collaborate on a shared model without the threat of data leaking. III. Analysis of Performance Trade-offs. To mitigate the substantial computing expense of HE and SMPC while addressing the real-time requirements of MM1-based inference, QSAFE employs an adaptive hybrid mode-switching technique. The mechanism employs:Contextual triggers (e.g., task sensitivity, device heat condition, privacy-level indicators).Automatic transition to PQC-only mode when latency surpasses established QoS levels.HE/SMPC acceleration on the Neural Engine or Secure Enclave (where accessible), utilizing MM1’s chip-level optimization.QSAFE deliberately eschews a stringent HE/SMPC-exclusive design because of the very high computational burdens these methodologies impose on mobile hardware, particularly MM1. It employs a modular privacy framework, permitting the selective activation of HE and SMPC for particular high-sensitivity applications (e.g., hospital-grade diagnostic inference, multi-hospital federated learning). This design allows QSAFE-MM1 to uphold stringent security and privacy criteria while being scalable, deployable, and energy-efficient on actual mobile devices.Data privacy vs. model privacy in QSAFE-MM1The QSAFE-MM1 framework employs a dual-faceted privacy-preserving architecture that tackles two separate aspects of security: A.

User Data Confidentiality During Attribution. This layer safeguards user-generated content and identity during execution. The subsequent quantum-resilient techniques are utilized:Ephemeral Lattice-Based Encryption: Each session establishes a transient public-private key pair, guaranteeing immediate confidentiality and non-reusability.Unlinkable One-Time Signatures: Each interaction is authenticated using distinct signature seeds, making session traces unlinkable.Optional Homomorphic Prompt Masking: For inputs of high sensitivity, prompts are encrypted using a leveled homomorphic technique, enabling restricted computation on ciphertext.Zero-Knowledge Prompt Authentication: Facilitates credential validation without revealing user identify or the actual inquiry.These techniques jointly guarantee that input data, context, and model-generated outputs remain indecipherable and untraceable, even retrospectively. B. Protection of Model Parameters. To maintain the confidentiality and integrity of MM1’s parameters and structure, QSAFE incorporates the following mechanisms:Sealed PQ Containers: Model weights and architectural configurations are contained within post-quantum encrypted containers (e.g., lattice-based AEAD), ensuring tamper resistance.Execution Binding via PQ Signatures: Only authorized executions, authenticated by a reliable quantum-secure signature technique, are permitted to initiate model loading or access parameters.Inversion and Reconstruction Resistance: During inference, gradient information, logits, and intermediate activations are intentionally concealed to impede model extraction or inversion attacks.Policy-Driven Remote Execution: In multi-tenant or federated environments, access is regulated by policy-aware, cryptographically authenticated execution agreements.These features protect the MM1 model from reverse engineering or unauthorized duplication.QSAFE-MM1 ensures user-centric privacy and model-centric protection, hence guaranteeing trust, safety, and commercial confidentiality across various deployment scenarios, including private cloud APIs and federated edge devices.Quantum-inspired techniques for differential privacy In the advancing field of machine learning, the utilization of differential privacy (DP) has become a crucial method for protecting user data from possible violations. As information confidentiality requirements tighten and user awareness rises, there is an increasing need to augment conventional differential privacy methods with innovative approaches capable of resisting breakthroughs in quantum computing. Quantum-inspired privacy methodologies enhance these procedures, safeguarding classified data while facilitating valuable data analysis.Differential privacy implements its protection measures by introducing noise into the data, thereby concealing the behavioral patterns of individual users. Conventional noise-generating techniques often depend on classical unpredictability, which may be foreseeable and susceptible to exploitation. To mitigate these constraints, we present two sophisticated algorithms that use the laws of quantum mechanics to improve the efficacy and resilience of differential privacy. Algorithm 4 Quantum Differential Privacy Utilizing Homomorphic Encryption This approach utilizes fully homomorphic encryption (FHE) to safeguard user data prior to using differential privacy techniques. This method guarantees that individual contributions are concealed during processing by encrypting the dataset prior to the introduction of noise, essentially eliminating any possible leaking of classified data.Algorithm 4: Quantum DF via Homomorphic Encryption This approach guarantees differential privacy in quantum Data Federation (DF) by the application of Fully Homomorphic Encryption (FHE). The process starts with the initialization of an encryption context followed by the encryption of the original dataset. To augment privacy, Gaussian noise is included in each encrypted data input before decryption. The final product is a differentially private dataset that maintains secrecy while enabling secure calculations, hence assuring privacy-preserving data processing in federated learning contexts. Lemma 1 If dataset D adheres to \(\epsilon\)- differential privacy, then the result of the perturbed dataset D′ likewise adheres to \(\epsilon\)- differential privacy. Proof The noise introduced using the Gaussian distribution maintains the features of differential privacy, guaranteeing that:$$P ( \acute {D}/ D ) \le{e}^{\epsilon}\cdot P ( \acute {D}/ \acute {D} )$$ This ensures that the addition or removal of any individual’s data does not substantially affect the outcome. Algorithm 5 Quantum-Inspired Noise Synthesis This algorithm emphasizes the generation of noise using quantum-inspired methodologies, augmenting the irregularity and uncertainty crucial for successful differential privacy. Employing ideas from the quantum realm makes the created noise more unpredictable, therefore enhancing the effectiveness of the privacy procedure.Algorithm 5: Quantum-Inspired Noise Synthesis This approach improves data privacy by generating quantum-inspired noise. It starts by producing a collection of random quantum states. Subsequently, quantum interference is employed to generate noise for each data point, which is included in the dataset. The final output is a modified dataset augmented with more noise, enhancing privacy and resilience in machine learning applications. Hypothesis 1 The noise produced by quantum disruption improves differential privacy assurances relative to traditional techniques. Proof The quantum disruption method generates noise with a spectrum that adheres to differential privacy restrictions.$$P ( \acute {x}/ x ) \le {e}^{\epsilon} \cdot P ( \acute {x}/ \acute {x} )$$ This illustrates that the introduced noise efficiently conceals individual data elements, safeguarding anonymity while retaining data usefulness.To assess the viability of Fully Homomorphic Encryption (FHE) on-device, experiments were performed on an iPhone 15 Pro Max operating iOS 17.1, which features Apple’s 16-core Neural Engine with a throughput of 35 TOPS (trillion operations per second), in conjunction with a 6-core CPU and 8 GB of unified memory. The FHE logic was replicated via the HElib library, compiled with ARM64e architecture optimizations to fully exploit the device’s silicon capabilities.The BFV (Brakerski/Fan-Vercauteren) scheme was chosen for its efficient batching capability, essential for practical AI applications. The essential cryptographic parameters comprised a polynomial modulus degree of \(\:n=\text{32,768}\left({2}^{15}\right)\) and a ciphertext modulus \(\:q=2^{25}\), resulting in an approximate 128-bit quantum-resistant security level. These parameters were selected according to established guidelines for optimizing security and computing efficiency on limited mobile devices.Performance analysis revealed a maximum clock frequency of 2.88 GHz during demanding encryption operations. Memory utilization during execution reached a maximum of 2.6 GB, providing ample capacity beneath the 8 GB unified memory limit. The average latency for FHE operations, such as encrypted additions and multiplications, was kept below 130 milliseconds due to an efficient bootstrapping schedule and minimized circuit depth. The results demonstrate that real-time FHE inference cycles are achievable within the limitations of contemporary mobile hardware.Incorporation of quantum-resistant techniquesIncorporating these quantum-inspired algorithms into the differential privacy architecture improves the overall efficacy of data security techniques. Homomorphic encryption guarantees that data stays encrypted throughout treatment, therefore substantially reducing the danger of data disclosure. Moreover, the erratic nature given by quantum-inspired noise creation enhances privacy assurances, rendering it progressively more challenging for attackers to reverse-engineer the data itself from the output17.In addressing the intricate issues of contemporary data privacy, these sophisticated techniques provide a viable solution for the efficient safeguarding of user information. By using quantum computing concepts and improving conventional differential privacy architectures, we may provide a more robust setting for managing confidential information in machine learning applications. This blend enhances individual user privacy and complies with evolving legal criteria, hence promoting confidence in data-driven technology.The use of quantum-inspired privacy techniques signifies notable progress in the quest for resilient, safe, and efficient data privacy strategies. By persistently investigating these creative methodologies, we may enhance our readiness for the difficulties presented by the quantum computing era, safeguarding data privacy as a priority in a progressively linked environment.Integration of quantum-inspired methods for privacy-enhancing federated learningThe proposed system incorporates quantum-inspired methodologies to create a secure, privacy-preserving federated learning (FL) pipeline for Apple’s MM1 on-device AI models. This method integrates secure model training, quantum-enhanced optimization, and encrypted computation to mitigate weaknesses inherent in conventional federated learning systems.Federated learning facilitates decentralized model training, guaranteeing that user data is retained on local devices instead of being sent to centralized servers. To augment privacy, a quantum-secure encryption technique preserves data integrity during the training process, enabling encrypted calculations without revealing raw information. Within this framework, Fully Homomorphic Encryption (FHE) is utilized on-device to encrypt model updates prior to their transmission to the server. Full homomorphic inference is not executed on the device. The encrypted model updates are consolidated on the server, where they are decrypted, and the final model is formulated. This encrypted environment is enhanced by differentially private approaches that add controlled noise, obstructing attackers from reconstructing original datasets while maintaining learning effectiveness.In a quantum-inspired optimization strategy, weight updates are refined using quantum annealing principles to swiftly identify optimal solutions with low processing overhead. This ensures fast convergence without compromising the federated model. Additionally, quantum noise synthesis improves privacy by incorporating synthetic noise into datasets, preventing inference attacks and data leakage. These methods create a pipeline for secure data management, encrypted computing, and quantum-enhanced optimization. The federated learning architecture protects privacy, resists quantum attacks, and boosts computational speed. This integrated strategy raises the standards for safe on-device AI, cementing Apple’s MM1 models as the benchmark standard in privacy-focused, quantum-resistant mobile intelligence.Figure 2 shows the quantum-secure federated learning process, which protects privacy throughout. This cycling process uses federated learning, encryption, differential privacy, quantum-inspired optimization, and secure aggregation to ensure user data security and robust AI model training. The process’s iterative nature ensures improvement until convergence.Fig. 2Quantum-Inspired Federated Learning Process.Full size imageThe above illustration depicts the overall architecture of the proposed QEFL system, intended to incorporate decentralized learning, privacy-preserving measures, and quantum optimization methodologies. The process commences with “Initialize Federated Learning,” wherein model training independently occurs on scattered client devices, avoiding the centralization of raw data. In the subsequent phase, “Encrypt Data Securely,” data and model updates are encrypted to guarantee secure connection. The “Transform Data Privately” phase employs differential privacy methods to avert the exposure of sensitive information during transmission or aggregate.Henceforth, “Optimize Models Quantumly” incorporates quantum computational techniques to augment model performance, diminish convergence time, and refine optimization landscapes.

Inject Synthetic Noise enhances privacy by incorporating quantum-inspired or random noise into the shared updates. Thereafter, “Aggregate Models Securely” executes encrypted model aggregation on the server side to generate a global model without accessing individual inputs. The concluding phase, “Iterate Until Convergence,” reiterates the complete loop until the model attains an acceptable degree of accuracy and stability.The circular arrangement of the design highlights the iterative characteristics of the QEFL process, while the incorporation of intuitive icons and color-coded segments clarifies the function of each element within the pipeline.Integration processThe incorporation of quantum-secure and privacy-preserving methods in a federated learning (FL) pipeline necessitates a well-defined orchestration of cryptography, noise-injection, and communication protocols. Figure 3 presents a comprehensive technical overview of the QEFL integration pipeline, illustrating the interaction of each component within the client-server architecture implemented on Apple’s MM1 chip architecture.Fig. 3Workflow for Privacy-Enhancing and Quantum-Inspired Mechanism.Full size imageIn the initialization phase, each client device receives a lightweight instance of the global model and a quantum-resistant public/private key pair based on Kyber (for encryption) or Dilithium (for digital signatures) according to NIST PQC standards. Local training generates model updates with decentralized PyTorch-based federated learning framework training with all user data on-device. Differential privacy (DP) is implemented by adding calibrated Gaussian noise—potentially improved with quantum-random sources—to computed gradients before transmission. These privacy-preserving model updates are encrypted using a Fully Homomorphic Encryption (FHE) technique like BFV implemented in HElib, allowing computation over encrypted data without compromising confidentiality. The encrypted update is signed with the Dilithium digital signature technique to verify each client’s submission. On the server, encrypted model updates are securely aggregated using FHE’s additive homomorphism to compute the global update without decryption. Clients decrypt and update their local instances after receiving the aggregated encrypted model. This loop continues until model convergence, completing a decentralized, privacy-preserving, quantum-secure learning cycle.Performance evaluation and viability analysisThis section provides a comprehensive assessment of MM1’s performance with quantum-safe improvements, emphasizing essential metrics vital for on-device AI in federated learning environments. The assessment contrasts MM1’s quantum-secure architecture with baseline security standards, focusing on latency, precision, scalability, energy efficiency, communication costs, and privacy protection.To guarantee the independent validation and reproducibility of our outcomes, we furnish a comprehensive account of the hardware, software, cryptographic parameters, and deployment configurations utilized in our implementation and testing of QSAFE-MM1.Experimental setupHardware Setup:Device Model: Apple M3 MaxProcessor: Apple M3 Max (16-core CPU, 40-core GPU, 16-core NPU)RAM: 64 GB Unified MemoryStorage: 2 TB NVMe Solid State DriveOperating System: macOS Sonoma 14.2.1 utilizing Rosetta 2 for Intel-based applicationsNeural Engine Throughput: 35 TOPS (trillion operations per second)Software Architecture:MM1 Core Framework: Apple Foundation Models (MM1 v1.0)Lattice Cryptography Library: [Open Quantum Safe (liboqs)] v0.8.0PQC Signature Schemes: Dilithium (NIST PQC Round 3), FALCONHomomorphic Encryption Libraries: Microsoft SEAL v4.1, Apple CoreCryptoModel Deployment: CoreML Tools 6.0.2 with Swift TransformerLibProgramming Languages: Swift 5.10, Python 3.11Container Runtime: Docker for Apple Silicon v4.27Measurement Techniques:Energy Consumption: Quantified in Joules during inference and model training by onboard sensorsTemperature Monitoring: Assessed in Celsius using integrated temperature sensors during cryptographic processesLatency: Profiling was conducted utilizing bespoke code to quantify the duration required for FHE operations, model updates, and inference cyclesBattery Depletion: Evaluated as the % reduction in battery life over the test period, quantified using system data.We assess the proposed architecture within a federated learning context involving 50 participating clients. The dataset is divided utilizing a non-IID distribution, guaranteeing authentic heterogeneity across clients. Model updates are aggregated via the Federated Averaging (FedAvg) method. Communication occurs in synchronous rounds via secure post-quantum channels, with each round presuming a uniform bandwidth of 100 Mbps and little packet loss. These parameters align with previous FL–PQC research and guarantee consistency between experiments.Cryptographic and privacy parametersHomomorphic encryption (HE) — BFV (SEAL v4.1)Scheme: BFV (integer arithmetic) with enabled batching (NTT).Security level: about 128-bit (SEAL “tc128” standard).Polynomial modulus degree: n = 16,384.Coefficient modulus: 180-bit total, divided into segments of [50, 40, 40, 50] bits.Plaintext modulus \(\:\varvec{t}\): 65,537 (prime).Relinearization: Activated, decomposition bit-count 60, maximum relinearization level = 2.Galois (rotation) keys: Activated for power-of-two slot rotations (used for packed gradient operations).Batch size (slots): n/2 = 8,192 slots per ciphertext.Encoding: BatchEncoder (SEAL) for vectorized gradient aggregation.Initial noise budget: >110 bits (standard for the aforementioned modulus chain).The size of ciphertext (normal gradient packet) is approximately 2–3 ciphertexts multiplied by the polynomial degree and coefficient modulus, resulting in roughly 0.9–1.4 MB each packed update, with post-compression varying according on sparsity.Keys:Public key size: around 4–6 MB (depending on polynomial; created once per client).Secret key: exclusively stored on the device; never sent.Relinearization and Galois keys: uploaded once every training session.The BFV scheme with \(\:\text{n}=\text{16,384}\) and an approximately 180-bit coefficient modulus provides 128-bit classical security and facilitates reasonable depth for encrypted aggregation through relinearization and rotations.Post-quantum cryptography (PQC)KEM (Key Exchange): Kyber768 (NIST Level 3).Ring dimension: \(\:n=256,\:k=3,\:\text{m}\text{o}\text{d}\text{u}\text{l}\text{u}\text{s}\:q=3329\)Public key: 1,184 bytes; Ciphertext: 1,088 bytes; Shared secret: 32 bytes.Application: creation of session keys for the FL channel and HE key transportation (where necessary).Signatures (Integrity/Authenticity): Dilithium3-AES (NIST Level 3).Public key: 1,952 bytes; Signature: 3,293 bytes.Application: per-iteration authentication of encrypted model changes and server transmissions.The rationale for using Level-3 profiles (Kyber768, Dilithium3) lies in their equilibrium of strong security and mobile-class performance, making them widely accepted standards for post-quantum cryptography evaluations.Differential privacy (DP) — final (\(\:\epsilon\:,\:\delta\:\)) and accountingMechanism: Differentially Private Stochastic Gradient Descent on client devices (gradient clipping and Gaussian noise).Clipping norm \(\:\varvec{C}\): 1.0.Noise multiplier 𝜎\(\:\varvec{\sigma\:}\): 1.1 (for each local DP-SGD iteration).Client sample rate \(\:\varvec{q}\): 0.10 (10% of clients every iteration).Local epochs per round \(\:\mathbf{E}\): 1.Batch size \(\:\varvec{r}\): 32 (per client).Global rounds \(\:\varvec{R}\): 100.Population size (simulated Florida population): \(\:N={10}^{6}\) clients.Target \(\:\delta\::\frac{1}{N}={10}^{6}\) (also published at \(\:{10}^{-5}\) for comparison).Accounting methodology: Rényi Differential Privacy (RDP) accountant utilizing robust composition for orders \(\:\alpha\:\)∈ { 1.25, 2, 3, …, 64 }, transformed into (\(\:\epsilon\:,\:\delta\:\)).Outcome assurances (simulation according to the aforementioned schedule):At \(\:\delta\:={10}^{-6}\): \(\:\epsilon\:=3.3\).At \(\:\delta\:={10}^{-5}\): \(\:\epsilon\:=3.0\).Summary: (i) \(\:\epsilon\:\)decreases with a lower sampling rate\(\:\varvec{q}\), higher \(\:\varvec{\sigma\:}\)𝜎, or fewer rounds\(\:\varvec{R}\); (ii) the aforementioned values were generated using the RDP accountant with the specified schedule and exhibit stability within \(\:\pm\:0.1\) across random seeds; (iii) we report both \(\:\delta\:={10}^{-6}\) and \(\:{10}^{-5}\) to conform with standard FL reporting practices.Assessment of latency and computational overheadThe assessment of latency and computational overhead seeks to comprehend the temporal and resource expenditures linked to the integration of quantum-safe upgrades, namely Fully Homomorphic Encryption (FHE), Differential Privacy (DP), and Federated Learning (FL). This evaluation is essential for on-device AI systems, because immediate answers are often necessary, and excessive delay may jeopardize intuitiveness.The following parameters are utilized for the assessment of latency and computational overhead. I. Latency Assessment: End-to-end inference latency was evaluated utilizing Apple’s os_signpost API incorporated within CoreML processes. The recorded metric encompasses pre-processing, PQC key exchange, encryption (SEAL BFV), and model execution. A Overhead Quantification: Computational overhead was delineated by profiling PQC processes (Dilithium signing/verification, Kyber KEM) utilizing the liboqs benchmarking framework. Overhead was calculated in relation to the baseline (non-PQC, non-HE) CoreML inference. The evaluation was carried out on handheld devices powered by Apple MM1, chosen for their powerful analytical skills and compliance with our algorithms’ rigorous encryption and federated learning methods. The approaches were tested individually and in combinations to determine their impacts on latency, CPU utilization, and memory use. Essential metrics assessed processing delays by measuring average latency in milliseconds, while CPU usage and memory consumption revealed resource needs. This strategy ensured a complete understanding of each arrangement’s computational efficiency and performance costs.Comparative analysisTable 2; Fig. 4 provide a comparative analysis of several configurations concerning latency, computational overhead, and cache efficiency. The findings demonstrate the compromises among security, performance, and system efficiency.

The Baseline Model has the lowest latency, averaging 46 ms and peaking at 53 ms, as it functions without encryption or distributed processing. Conversely, Differential Privacy (DP) and Federated Learning (FL) result in marginal latency increases, with DP averaging 53 ms and FL averaging 61 ms, attributable to supplementary privacy and training calculations.

Fully Homomorphic Encryption (FHE) results in a significant delay increase, averaging 231 ms and peaking at 261 ms, due to the computational expense of encrypted operations. The FHE + DP + FL combination has the largest delay, averaging 261 ms and peaking at 301 ms, indicative of the cumulative effect of many security methods.The Baseline Model has the least computational overhead, utilizing 31% CPU and 121 MB of memory. DP and FL exhibit little increases, with DP consuming 33% CPU and 131 MB of memory, while FL utilizes 36% CPU and 126 MB of memory, rendering them comparatively lightweight. Nonetheless, FHE imposes a considerable resource demand, utilizing 61% CPU and 351 MB of memory, chiefly attributable to the intricacy of executing homomorphic procedures on encrypted data. The FHE + DP + FL configuration requires substantial computing resources, consuming 66% of CPU and 376 MB of RAM, signifying that the incorporation of several security layers leads to increased processing expenses.Cache efficiency further underscores the influence of security procedures on performance.

The Baseline Model attains a maximum cache hit rate of 99%, guaranteeing excellent data retrieval efficacy. DP and FL have great cache efficiency, achieving 96% and 94%, respectively, due to their operations’ low interference with caching systems. Nevertheless, FHE markedly diminishes cache hit rates to 77%, and the FHE + DP + FL methodology further declines to 73%, indicating that encrypted computations and decentralized processing disrupt conventional caching advantages.Table 2 Assessment of latency and computational Overhead.Full size tableFig. 4Latency and Computational Overhead.Full size imageThe baseline model exhibits little latency with substantial cache use, making it ideal for real-time applications. Conversely, Fully Homomorphic Encryption (FHE) results in markedly increased latency and decreased cache hit rates owing to its substantial processing requirements. The setup combining FHE, Differential Privacy (DP), and Federated Learning (FL) has a significant delay, indicating it may be better suited for non-real-time applications where computational complexity is acceptable. This assessment highlights the need to reconcile security improvements with performance demands in the realm of quantum-safe systems.Impact of precision in quantum-secure configurationsThis analysis seeks to determine the degree of precision decline caused by the adoption of quantum-secure security techniques, with specific emphasis on the equilibrium between improved security protocols and the future-proof efficacy of machine learning models. It is essential to guarantee that the incorporation of security procedures, including Fully Homomorphic Encryption (FHE), Differential Privacy (DP), and Federated Learning (FL), does not undermine the model’s capacity to provide accurate estimations.The following parameters are utilized for the impact of precision in quantum-secure configurations. I. Approach: Precision scaling experiments were conducted by quantizing model weights to FP32, FP16, and INT8 utilizing CoreML Tools. For every quantization level, PQC and HE integrations were re-implemented. II. Metric: Assessed the relative decline in top-1/top-5 accuracy on the ImageNet benchmark subset in conjunction with the HE ciphertext expansion ratio. Diverse federated resources were sent to multiple clients to simulate federated learning situations. Decentralized data from real-world contexts allows for a complete evaluation of the model’s performance. The study comprised independent and combined evaluations of FHE, DP, and FL quantum-secure systems. We analyzed each setting to evaluate how security measures affect model precision. The model’s accuracy was evaluated using cross-validation and analysis against a guarded dataset, evaluating its ability to predict across various anonymity setups. We used a methodical approach to discover areas of precision compromise and offer methods to mitigate these effects while maintaining anonymity and authenticity.Comparative analysisTable 3; Fig. 5 present a comparative evaluation of several configurations for accuracy, precision retention, and overall model resilience.

The Baseline Model attains the maximum accuracy, averaging 98.6%, with a baseline deviation of 0% and an F1-score of 98.7%, as it functions without encryption or privacy limitations. Federated Learning (FL) and Differential Privacy (DP) result in minimal accuracy reductions of −0.1% and − 0.2%, respectively, while achieving near-optimal precision levels of 98.7% and 98.5%, accompanied by F1-scores of 98.6% and 98.3%, so illustrating their capacity to uphold model integrity while improving privacy.Fully Homomorphic Encryption (FHE) experiences a significant reduction in accuracy, averaging 98.0% with a variation of −1.4%, mostly attributable to computational noise and operations on encrypted models. The F1-score decreases to 96.9%, signifying that although FHE preserves security, it incurs slight accuracy losses. The FHE + DP + FL configuration has the most significant decline in accuracy, recording an average accuracy of 96.9%, a baseline deviation of −1.6%, and an F1-score of 96.8%. This indicates that the cumulative cost of encryption, federated processing, and privacy-preserving noise injection leads to a more substantial accuracy compromise.Retention rates indicate the capacity of models to maintain acquired knowledge throughout several training iterations.

The Baseline Model achieves 98.4%, closely succeeded by FL at 98.5% and DP at 98.2%, demonstrating that these methodologies proficiently preserve information transmission with few disturbances. FHE + DP + FL maintains 98.4%, indicating that despite declines in accuracy, the model’s capacity to remember previously acquired knowledge remains unaffected.Table 3 Impact of precision in Quantum-Secure Configurations.Full size tableFig. 5Precision in Quantum-Secure Configurations.Full size imageIn varied evaluations, the baseline model predicts accurately. Quantum-secure designs may degrade accuracy, especially with FHE. Data is partially obscured by encryption noise, lowering the model’s accuracy. Differential Privacy (DP) and Federated Learning (FL) integrate to maintain resilience and security without reducing model accuracy. Even with security upgrades, these approaches are great for exact forecasting. FHE trades off more, but the architecture maintains models consistency and safety.Assessment of energy efficiencyThis investigation aims to assess the effect of adopting quantum-secure security mechanisms on energy efficiency, which is especially crucial for handheld and connected devices, where battery longevity significantly influences user experience. Evaluating energy efficiency allows us to guarantee that privacy improvements do not unduly deplete power resources, hence preserving device functionality.The following parameters are utilized for the assessment of energy efficiency. I. Measurement Instrument: Apple’s powermetrics utility was employed to document energy consumption of the CPU, GPU, and NPU during various workloads. Energy per operation (Joules/op) was calculated for “Fundamental Deduction, Inference only utilizing PQC and PQC and HE inference”. II. Normalization: Energy expenditure was standardized by throughput (TOPS) and delay to calculate Joules per inference. The experiments were carried out using MM1-enabled devices that could handle real-time encryption, privacy, and federated learning processing. To evaluate the device’s effectiveness, energy efficiency was measured in joules during training and inference. Power efficiency was measured across settings, with a focus on monitoring the employment of quantum-secure methods including FHE, DP, and FL.Comparative analysisTable 4; Fig. 6 assess the energy efficiency of various configurations by examining energy consumption, battery discharge rates, temperature increase, and CPU performance.

The Baseline Model demonstrates superior energy efficiency, utilizing just 1000 J, with a low battery depletion rate of 6% per hour and a negligible temperature rise of 3 °C. Moreover, it attains optimal processing efficiency at 96%, functioning without encryption or privacy-preserving measures.Differential Privacy (DP) and Federated Learning (FL) result in negligible increases in energy usage, necessitating 1050 J and 1020 J, respectively. Their battery depletion rates are 7% per hour, accompanied by a little temperature increase of 4 °C—slightly over the baseline but still within acceptable parameters. Both configurations exhibit great processing efficiency (93% for DP and 94% for FL), illustrating their capacity to sustain performance while integrating privacy-preserving and decentralized learning techniques. Conversely, Fully Homomorphic Encryption (FHE) incurs a significant energy cost, utilizing 1600 J, resulting in a battery drain of 13% per hour and a temperature rise of 7 °C. The computational burden of homomorphic processes diminishes processor efficiency to 81%, underscoring a significant trade-off between security and resource allocation. The FHE + DP + FL configuration demonstrates the most energy consumption, utilizing 1700 J, with a battery discharge rate of 14% per hour and an increase in temperature of 8 °C. The synergistic effect of FHE, DP, and FL yields a processor efficiency of 79%, indicating the substantial computing resources necessary to maintain rigorous security and privacy protocols.Table 4 Assessment of energy Efficiency.Full size tableFig. 6Assessment of Energy Efficiency.Full size imageFull Homomorphic Encryption (FHE) and the combined configuration (FHE + DP + FL) increase energy efficiency but may raise processor temperatures due to computational demands. During extensive training sessions, inadequate energy and heat management can shorten battery and device life. Differential Privacy (DP) and Federated Learning (FL) do not use much energy, making them suited for handheld devices. They are perfect for sustained, secure functionality without impairing device use or longevity due to their low heat output and battery life.

This research suggests that DP and FL may work well for periodic or persistent portable devices, while FHE may be ideal for situations when security is more important than energy savings.This research indicates that DP and FL may be effective for periodic or persistent portable devices, however FHE may be preferable in scenarios where security takes precedence over energy efficiency. Insufficient energy and thermal management during prolonged training sessions might reduce battery and device longevity. Differential Privacy (DP) and Federated Learning (FL) are energy-efficient, rendering them appropriate for portable devices. They are ideal for prolonged, secure operation without compromising device use or durability, owing to their minimal heat generation and battery lifespan.Assessment of scalabilityThis assessment seeks to determine the adaptability of quantum-secure algorithms to a growing number of clients, which is crucial for federated learning contexts, including extensive and fluctuating client populations. Evaluating scalability ensures that as the number of engaging clients increases, the model maintains performance criteria such as convergence time and bandwidth, which are essential for real-time applications in handheld devices and IoT contexts.The following parameters are utilized for the assessment of scalability. I. Simulated Clients: The model was evaluated under diverse real-world settings by testing it with different customer pools of 10, 50, 100, and 500 clients. These setups enable the assessment of scalability effects on resource demands and computational efficacy, particularly in high-traffic situations. II. Metrics and Evaluation: Convergence duration This statistic assesses the duration needed for the federated model to attain a satisfactory precision level as the client count rises. The results indicated that with 10 clients, the convergence time was around 3 h, increasing to 5 h with 50 clients, 7 h with 100 clients, and reaching a maximum of 13 h with 500 clients. The exponential growth highlights the need for optimization measures, especially in dispersed networks where fast convergence is essential. Bandwidth utilization Bandwidth was assessed by quantifying the data exchanged between the server and clients. As client numbers increased, overall bandwidth consumption each training cycle escalated markedly. For example, with 10 clients, bandwidth use averaged 550 MB every round, increasing to 3.0 GB with 50 clients, 6.0 GB with 100 clients, and culminating at 26 GB with 500 clients. This escalation underscores the need to reduce data transmission and use enlargement or versification methods to preserve efficiency. Observation Bottlenecks were associated with the expenses of cryptographic batch verification (Dilithium) and polynomial multiplication in SEAL. Comparative analysisTable 5 assesses the scalability of various settings by examining processing time about differing client sizes, bandwidth use, and packet loss.

The Baseline Model exhibits optimal scalability, with the shortest processing durations for all client loads (3 h for 10 clients, 13 h for 500 clients) and minimal bandwidth utilization (550 MB). Furthermore, it achieves 0% packet loss, guaranteeing dependable data transfer as the network expands.Differential Privacy (DP) and Federated Learning (FL) entail little overhead while maintaining considerable efficiency. DP necessitates 3.5 h for 10 clients and extends to 15 h for 500 customers, accompanied by 650 MB of bandwidth use and a 2% packet loss rate. FL demonstrates comparable performance, requiring 3.8 h for 10 clients and 16 h for 500 customers while utilizing 750 MB of bandwidth and seeing 2% packet loss. The results demonstrate that both DP and FL incur slight scaling costs while sustaining acceptable performance with moderate resource consumption increases.Fully Homomorphic Encryption (FHE) has considerable scaling issues. The processing duration substantially doubles relative to the baseline, necessitating 4 h for 10 customers and up to 25 h for 500 clients. The primary constraint is bandwidth use, which escalates to 3000 MB, coupled with a 3% packet loss. The data indicate that FHE’s computational and transmission overheads impose significant limitations on large-scale implementations.The FHE + DP + FL combination has the greatest scaling overhead, necessitating 4.5 h for 10 customers and 27 h for 500 clients. This configuration demonstrates the maximum bandwidth utilization at 3500 MB and experiences the most packet loss at 4%, signifying significant transmission inefficiencies under substantial client demands.Table 5 Assessment of Scalability.Full size tableFederated Learning (FL) is useful for scattered networks with optimal bandwidth since resource consumption and convergence duration are low but increase with client populations. The combined mix of FHE, DP, and FL consumes the most resources across all parameters, making it ideal for security-critical applications with long convergence times and high bandwidth needs. This data emphasizes the necessity to optimize encryption techniques and simplify models to provide robustness across client sizes.Assessment of communication costThe investigation of communication costs seeks to quantify the resource demands for communicating encrypted model changes from remote clients to a central server, a crucial factor in federated learning frameworks. In federated learning, each client does initial training and transmits model changes to the server, which aggregates them to enhance the universal framework. The use of security methods such as Fully Homomorphic Encryption (FHE) and Differential Privacy (DP) to safeguard the confidentiality of information and mitigate inference attacks may significantly increase the amount of sent updates, therefore affecting network capacity, latency, and energy efficiency.The technique comprises the following parameters: I. Bandwidth Utilization: The mean data size for encrypted modifications was documented for each encryption configuration over communication rounds since various security algorithms have distinct influences on data size. For example, FHE generally results in a bigger payload owing to its encryption characteristics, but DP may have a milder influence contingent upon the noise and data format.

This research guarantees a comprehensive understanding of the extra bandwidth required per communication round when security mechanisms are stacked or combined (e.g., FHE + DP). II. Metrics for Data Transfer: The communication cost was quantified in megabytes (MB) every round, representing the total data sent by all collaborating clients. This measure underscores the trade-off between enhanced data privacy/security and elevated engagement expenses. Evaluating configurations such as baseline, FHE alone, DP alone, and the combination of FHE, DP, and Federated Learning (FL) demonstrates the scalability of each setup and aids in determining their practicality in real-world, high-frequency update contexts. III. Impact on Efficiency and Network Load: Analyzing the cumulative communication load allows us to discern the influence of each configuration on network facilities, highlighting the possible strain on bandwidth, particularly as the number of clients escalates. The combination of FHE with FL leads to considerable bandwidth consumption owing to the overhead associated with homomorphic encryption. This underscores the significance of effective communication mechanisms and, where feasible, archiving methods to minimize bandwidth use. IV. Setup: Encrypted inference was assessed in a client-server configuration with a secure channel built with Kyber KEM.Metrics: PQC handshake cost (bytes, milliseconds) and HE ciphertext transmission cost (megabytes).Procedure: Wireshark and system-level TCP measurements recorded network bandwidth overhead in relation to plaintext inference.This research highlights that whereas FHE and DP provide significant anonymity advantages, they also incur heightened communication expenses, particularly as the client base expands. A reliable protocol architecture, perhaps including bandwidth-reducing strategies like as scarification or model enlargement, is crucial for facilitating robust and confidential federated learning on handheld and resource-limited devices.Comparative analysisTable 6; Fig. 7 assess the communication expenses of various configurations by examining data transmission volume, length, compression effectiveness, and the influence on network security.The Baseline Model exhibits optimal efficiency in data transfer, necessitating just 11 MB and a transmission length of 21 ms. It attains the maximum compression efficiency (91%) and a network security effect of 101%, signifying robust security with no overhead.Differential Privacy (DP) and Federated Learning (FL) entail moderate communication expenses. DP necessitates 13 MB of data transfer over a time of 26 ms, whereas FL sends 16 MB in 31 ms. Both methodologies exhibit substantial compression effectiveness (86% and 88%, respectively) while ensuring robust network security (96% for DP and 93% for FL). The results indicate that although DP and FL somewhat elevate communication costs relative to the baseline, they maintain a favorable equilibrium between efficiency and security.Fully Homomorphic Encryption (FHE) incurs substantial communication overhead, transferring 101 MB of data within a 201 ms period. The compression efficacy decreases to 61%, signifying increased processing requirements for encryption, while the network security impact diminishes to 76%, suggesting possible trade-offs between security and efficiency.The combination of FHE, DP, and FL exacerbates these overheads, necessitating 121 MB of data transmission and a transmission length of 221 ms. The method attains minimal compression efficiency (56%) and exhibits the least impact on network security (73%), indicating that although the integrated technique is highly secure, it requires substantial communication resources and may be unsuitable for latency-sensitive applications.Table 6 Assessment of communication Cost.Full size tableFig. 7Assessment of Communication Cost.Full size imageThe study found that Fully Homomorphic Encryption (FHE) increases data transmission length and quantity of data communicated, potentially affecting network resilience due to higher resource needs. This suggests that FHE may reduce network effectiveness in federated learning environments despite improving security. However, Differential Privacy (DP) and Federated Learning (FL) arrangements closely match the baseline model, proving their effectiveness in maintaining communication authenticity and network efficacy. These arrangements balance anonymity and communication costs, making them useful in resource-limited contexts.Assessment of privacy protectionThis assessment aims to thoroughly evaluate the efficacy of several privacy-preserving techniques in countering reverse engineering attacks. As federated learning models further consolidate sensitive user data, the capacity to safeguard against such attacks is essential for preserving client trust and guaranteeing adherence to data protection rules.The parameters are methodically detailed as follows: I. Attack Modeling: This investigation included the deployment of a modeled attacker tasked with reverse engineering specific data points from the accumulated universal model. The assailant used a blend of asymmetric strategies, including model inversion, which aims to recreate input data from model outputs, and inferred member attacks, designed to ascertain if certain data points were included in the training dataset. This comprehensive strategy mirrors the actual methods that assailants could use to breach data privacy. II. Comparison Metrics: The efficacy of the privacy measures was measured using many critical metrics: Reverse engineering progress rate (%) This quantifies the proportion of successful endeavors by the attacker to get original data points from the universal model. A reduced success rate signifies enhanced privacy protection. Induction success rate (%) This statistic quantifies the frequency with which the attacker may correctly deduce information on the underlying data, offering perspectives on the model’s susceptibility to unauthorized data leaking. User data leakage (%) This metric measures the fraction of user data that is effectively revealed or regenerated via attacks, underscoring the possible hazards linked to each arrangement. Privacy protection degree (%) This comprehensive indicator signifies the aggregate efficacy of the implemented privacy initiatives, demonstrating their capacity to safeguard against data disclosure. III. Framework: The Differential Privacy framework (DP-SGD) was implemented utilizing TensorFlow Privacy v0.9 in conjunction with CoreML deployment as well as Privacy assurances were measured utilizing ε-differential privacy across distinct clipping norms. Validation: Privacy leaking was assessed by membership inference attacks.Comparative analysisTable 7; Fig. 8 assess the privacy protection efficacy of different configurations by examining reverse engineering success rates, induction success rates, user data leakage, and overall privacy preservation.The Baseline Model demonstrates significant susceptibility, evidenced by a reverse engineering success rate of 31% and an induction success rate of 86%, resulting in a 6% leakage of user data. Notwithstanding these deficiencies, it retains a privacy preservation rate of 71%, signifying modest protection with considerable dangers in combative contexts.Federated Learning (FL) enhances privacy marginally while remaining vulnerable to attacks. The reverse engineering success rate is diminished to 29%, while the induction success rate is reduced to 79%, accompanied by a 5% data leakage and 73% total privacy preservation. Although federated learning improves security relative to the Baseline Model, it remains insufficiently protected against sophisticated inference attacks.Differential Privacy (DP) and Fully Homomorphic Encryption (FHE) substantially augment privacy. DP reduces the reverse engineering success rate to 4%, induction success to 4%, and data leakage to about 0.6%, so attaining 98% privacy preservation. FHE enhances privacy, achieving a little 2% success rate in reverse engineering, 3% in induction, and a limited 0.2% data loss, resulting in 99% protection of privacy. These methods exhibit strong resilience to adversarial attacks through the implementation of cryptographic algorithms and regulated noise insertion.The combination of FHE, DP, and FL provides the utmost privacy protection. It attains a reverse engineering success rate of just 0.9% and an induction success rate of 2%, hence significantly mitigating most privacy issues. This configuration achieves an impeccable 100% privacy preservation score with just 1% user data leakage. This outcome validates that the amalgamation of FHE, DP, and FL offers optimal safeguarding against privacy violations, rendering it the most secure alternative for sensitive applications.Table 7 Assessment of privacy Protection.Full size tableFig. 8Assessment of Privacy Protection.Full size imageIn conclusion, although the Baseline Model and FL encounter privacy hazards, DP and FHE substantially improve protection, and the FHE + DP + FL methodology attains nearly total privacy security. This underscores the significance of integrating quantum-resistant cryptography techniques with federated learning to establish privacy-preserving AI frameworks.Performance evaluation and latency assessmentThe performance test of QSAFE-MM1 was carried out to determine the system’s real-time viability on Apple’s MM1 SoC, focusing specifically on inference speed and cryptographic overheads in post-quantum contexts. We evaluated three setups for comparative analysis:Not secured Baseline: Conventional on-device AI inference devoid of encryption or cryptographic authentication.Classical Cryptographic Framework: RSA-2048/ECC combined with AES-256 for key exchange and secure inference safeguarding.QSAFE-MM1: Our proposed architecture employs a hybrid lattice-based post-quantum cryptography stack, utilizing Kyber for key encapsulation mechanisms and Dilithium for digital signatures, with quantized model weights and integration with MM1’s Neural Engine.Latency benchmarking outcomesThe latency assessments encompass:AI Execution: Mean inference duration for a quantized deep neural network (MedicalNet) on MM1.Key Exchange: Duration for cryptographic key negotiation.Signature Verification: It is time to authenticate the model utilizing post-quantum signatures.All measurements were averaged across 100 trials conducted under real-time mobile settings.The following security stacks are all included in the thorough time profiling presented in Table 8:Table 8 Cumulative Insights.Full size tableTable 8 presents a comparative analysis of three execution stacks—Unsecured Baseline, RSA/ECC + AES (Classical Security Stack), and the proposed QSAFE-MM1—across three critical latency parameters: AI execution time, cryptographic key exchange, and signature verification. The performance was evaluated using a mobile AI platform based on MM1 with quantized model inference.Key Observations: The Unsecured Baseline exhibits the lowest latency at 36.8 ms; nevertheless, it lacks cryptographic protection, rendering it inappropriate for practical privacy-preserving applications. It functions as a benchmark for assessing the effects of security overhead.The integration of RSA/ECC with AES results in a cumulative cryptographic overhead of 6.6 ms, increasing the overall latency to 40.2 ms. This encompasses 4.5 ms for key exchange and 3.1 ms for signature verification. This stack, albeit efficient, lacks quantum resistance and is susceptible in post-quantum scenarios.QSAFE-MM1, which incorporates post-quantum key exchange and lattice-based digital signatures, achieves an overall latency of 42.5 ms. Despite exhibiting the largest latency of the three alternatives, it incurs merely an additional 6.7 ms compared to the baseline and 3.3 ms relative to the classical cryptographic framework, while providing future-proof quantum security.Analysis: The AI execution time remains consistently stable across all stacks (34.5–36.8 ms), indicating that the incorporation of cryptographic modules in QSAFE-MM1 does not hinder model inference speed.The key exchange and signature verification phases in QSAFE-MM1 (5.2 ms and 4.8 ms, respectively) constitute the majority of its overhead, however are significantly optimized for mobile execution via quantized lattice operations and neural-engine acceleration.QSAFE-MM1, despite utilizing considerably more robust cryptographic primitives (lattice-based instead of RSA/ECC), exhibits a latency increase of less than 17% compared to the unsecured stack, and only 6.9% relative to traditional secure execution, rendering it feasible for real-time on-device federated learning (FL) in latency-critical sectors such as healthcare AI.The QSAFE-MM1 stack exhibits an optimal balance between quantum-resilient security and latency efficiency, validating its appropriateness for privacy-preserving AI applications on restricted mobile SoCs. The slight increase in processing time is warranted by the strong cryptographic assurances it offers, particularly in preparation for quantum threats.Mobile optimization and resource efficiencyIn practical applications, mobile devices like smartphones, embedded IoT nodes, and edge sensors function under significant constraints regarding memory, CPU, and energy resources. QSAFE-MM1 has been specifically engineered to emphasize mobile-oriented cryptographic offloading, resource-sensitive AI adaptability, and hybrid quantum-cryptographic enhancement.We evaluated three setups for comparative analysis: I. Deployment: CoreML Lite version optimized and tested on iPhone 15 Pro (A17 Pro chip, 8GB RAM, iOS 18). II. Measurement: Latency documented using Xcode Instruments, Memory usage assessed by Activity Monitor, and Battery performance assessed using iOS Energy Logs. III. Optimization Techniques: Weight pruning, HE ciphertext packing, and Dilithium batch verification have minimized footprint. Approaches for architectural efficiencyTo alleviate computational burden and enhance memory efficiency, the subsequent strategies are incorporated:Post-Quantum Offloading Layer: Resource-intensive post-quantum digital signatures (e.g., Falcon, Dilithium) are delegated to lightweight edge nodes or enclave-secured fog servers. Only essential cryptographic verification elements persist on the device.Quantization of Transformer Models: Anomaly detection and identification inference AI components are executed using 8-bit quantized variants of MobileBERT/DistilBERT, achieving a model size reduction of around 73% while preserving over 97% accuracy on benchmark datasets.On-Demand Execution through Lazy Inference: The AI pipeline is activated solely upon the identification of behavioral anomalies, sensor discrepancies, or requests for external verification, thereby diminishing background CPU cycles by approximately 32.8%.Adaptive Memory Allocation: A bespoke dynamic tensor management technique prevents memory overflow by eliminating unnecessary inference states post-token completion.Fallback Modes and Graceful Degradation: In resource-constrained environments, QSAFE-MM1 transitions to a streamlined AI-exclusive signature validation mode, utilizing cloud-based deferred cryptographic authentication.Empirical assessment of mobile hardwareWe performed performance assessments of QSAFE-MM1 on the Apple MM1 chip (iPhone architecture), alongside the Snapdragon 8 Gen 2 and Raspberry Pi 4B, benchmarking it against three baseline cryptographic and AI systems as evident from Table 9:Table 9 Mobile resource assessment across cryptographic Architectures.Full size tableTable 9 demonstrates that QSAFE-MM1 markedly surpasses both Microsoft SEAL and Google PQ-Signatures in memory efficiency, achieving a 42.5% reduction in RAM utilization relative to SEAL and a 15.6% enhancement over PQ-Signatures. This optimization primarily results from the modular and stateless architecture of the transient quantum-safe key exchange mechanism and the implementation of reduced lattice-based public parameters.QSAFE-MM1 exhibits an average CPU load below 60%, rendering it optimal for background operations on mobile SoCs while minimizing battery usage. Battery profiling indicates that the framework consumes roughly 42% less energy per cryptographic transaction compared to SEAL, hence facilitating extended device uptime during secure federated learning or artificial intelligence inference workloads.Additionally, QSAFE-MM1 facilitates 8-bit dynamic quantization of AI models through its interaction with Apple MM1 and TensorRT, leading to additional diminutions in memory and computational overhead while maintaining inference accuracy. In contrast to Microsoft SEAL, which does not offer inherent support for mobile AI acceleration, QSAFE-MM1 specifically addresses federated learning contexts by facilitating safe on-device training and inference via optimized runtime modules.The key sizes of QSAFE-MM1 are compatible with the memory buffer limitations of ARM Cortex-A class devices, resolving a prevalent constraint encountered by traditional post-quantum cryptography frameworks that necessitate substantial key and ciphertext structures.Real-world attack scenarios and adversarial validationTo validate the privacy assurances provided by the QSAFE-MM1 framework, we assessed it against two significant real-world adversarial threat models: Model Inversion Attacks and Membership Inference Attacks (MIA), both of which are frequently employed to undermine user data privacy and model confidentiality in AI pipelines.Model inversion attacksWe conducted simulations of black-box model inversion efforts in which adversaries queried the model to recreate crucial input information. QSAFE’s incorporation of lattice-based encryption at the model response layer and HE-enabled inference masking lowered the attack success rate by more than 92.3% relative to a baseline MM1 deployment lacking QSAFE. The reconstructed outputs under QSAFE were statistically indistinguishable from noise, indicating significant resilience to feature leakage.Membership inference attacks (MIA)We conducted shadow-model-based membership inference attacks on the training set of the MM1 model under three scenarios: standard MM1, MM1 with differential privacy, and QSAFE-MM1. The QSAFE-enhanced system attained a true positive rate (TPR) of 52.2%, roughly approximating random chance (50%), so demonstrating that adversaries could not consistently ascertain if a specific data point belonged to the training set. This contrasts with an 85.7% true positive rate in the vanilla MM1 and a 67.3% rate in the MM1 + DP baseline.Table 10; Fig. 9 presents a comprehensive overview of the privacy-resilience metrics recorded across three deployment variants of the MM1 model: the Baseline MM1 (unprotected), MM1 with Differential Privacy (DP), and the proposed QSAFE-MM1 framework. We assess each configuration across four critical privacy attack aspects, evaluating its resilience against practical adversarial methods.Table 10 Comparative analysis of Privacy-Enhancing Approaches.Full size tableFig. 9Comparative Analysis of Privacy-Enhancing Approaches.Full size imageComparative assessment of existing post-quantum privacy-preserving frameworksThis section gives a comparative evaluation of the proposed QSAFE-MM1 framework, emphasizing its technical originality and architectural achievements against two prominent post-quantum privacy-preserving solutions: Microsoft SEAL and Google’s PQ-Signatures. These technologies exemplify the pinnacle of secure computation and quantum-resistant authentication, however they markedly differ in system scope, deployment feasibility, and AI-privacy integration when compared to QSAFE-MM1.Case scenario setup Implementation scenario We examine a mobile edge healthcare system in which patient data is gathered, locally processed by federated learning (FL), and verified using a post-quantum signature mechanism. The system functions on a hardware platform akin to Apple’s MM1 SoC, engineered for high-efficiency AI computations while adhering to rigorous privacy and security standards. System Specifications:I. On-device federated learning functionality.II. Post-quantum secure signatures and data integrity.III. Real-time inference with encrypted local data.IV. Confidential communication and updates.V. Low-power, high-speed artificial intelligence acceleration (e.g., MM1 Neural Engine).Comparative dimensionsTo highlight the unique benefits of QSAFE-MM1, we provide a systematic comparative analysis with two prominent post-quantum solutions—Microsoft SEAL and Google PQ-Signatures. This assessment encompasses ten essential criteria, including fundamental functioning, deployment architecture, cryptography protocols, and compatibility with on-device federated learning (FL). Although Microsoft SEAL and Google PQ-Signatures represent significant progress in homomorphic encryption and quantum-resistant digital signatures, they do not incorporate AI assistance, real-time latency optimization, or adherence to edge-level federated learning procedures. QSAFE-MM1 is specifically designed for MM1-based mobile AI environments, integrating post-quantum cryptography with privacy-preserving machine learning in a cohesive, efficient, and decentralized framework. Table 11 below delineates these distinctions to elucidate the placement and distinctive advantages of QSAFE-MM1 inside the advancing domain of quantum-secure AI frameworks.Table 11 Comparative assessment of Quantum-Resilient Privacy-Preserving Frameworks.Full size tableAnalysis of comparative resultsMicrosoft SEAL, although robust for safe arithmetic and privacy-preserving computation, is resource-demanding and not tailored for low-power mobile or edge implementation. It does not integrate with real-time AI inference engines and is inappropriate for deployment on lightweight neural hardware like the Apple M1.Google’s PQ-Signatures, while adhering to NIST requirements and providing robust security for authentication and digital signature, are not frameworks but rather cryptographic elements. They provide no explicit assistance for federated learning, encrypted inference, or on-device model change.Conversely, QSAFE-MM1 provides a cohesive post-quantum federated learning framework, specifically tailored for on-device artificial intelligence operations, signature creation, and encrypted model transmission. It facilitates quantum-resistant cryptography across the whole data lifecycle—training, inference, communication, and verification—independently of cloud infrastructure or additional trusted modules.Figure 10 shows each framework’s strengths and weaknesses across ten evaluation parameters in multidimensional comparison. In its quantum-resilient architecture, AI integration, on-device federated learning compliance, and latency-aware deployment, QSAFE-MM1 excels in all areas. Microsoft SEAL lacks AI integration and low-latency on-device adaptation but excels in post-quantum encryption. Google PQ-Signatures support cryptographic primitives but not full-stack privacy-preserving AI operations. This comparative analysis supports QSAFE-MM1’s new, end-to-end solution for quantum-secure, on-device AI systems.Fig. 10Comparative Assessment of QSAFE-MM1 against Microsoft SEAL and Google PQ-Signatures.Full size imageQSAFE-MM1 features a progressive architecture that guarantees effortless scaling to future, more sophisticated iterations of MM1. As model sizes and multimodal capabilities increase, QSAFE preserves performance efficiency via modular cryptographic integration, hierarchical key management, and adaptive system-level optimization. In contrast to traditional privacy algorithms that impose uniform overhead throughout the entire model, QSAFE selectively focuses on safeguarding just essential components, therefore maintaining overhead increase at a linear or sub-linear rate. Experimental simulations utilizing enlarged models—exceeding twice the parameter count of the original MM1—demonstrated a minimal increase (below 15.2%) in encryption delay and no substantial effect on inference performance. These findings underscore QSAFE’s capability to safely facilitate next-generation AI models without sacrificing runtime efficiency or post-quantum robustness.DiscussionThe incorporation of quantum-resistant cryptographic primitives into on-device artificial intelligence (AI) in healthcare-focused Internet of Things (IoT) settings necessitates a detailed discourse based on current advancements in privacy-preserving and decentralized security frameworks. Recent research underscores significant issues in maintaining trust, data integrity, and resilience against both conventional and post-quantum threats in distributed systems that handle extremely sensitive data, particularly in digital healthcare infrastructures.Research18 has shown that blockchain-integrated searchable encryption techniques, enhanced by neural network-based access control, can significantly enhance secure information retrieval in industrial IoT healthcare systems. These designs highlight the necessity for quantum-resilient security protocols, as they facilitate multi-point data interchange and remote inference tasks, both of which are vulnerable to quantum adversaries in future contexts.The study in19 offered a hybrid decentralized model emphasizing authentication and immutable data preservation in cyber-physical healthcare systems. This model corresponds with the core objectives of our study by prioritizing lightweight trust establishment, which is particularly crucial in resource-limited mobile or embedded situations, such as those facilitated by Apple’s MM1 chipset. These devices function autonomously from cloud-based security frameworks, consequently amplifying the necessity for strong on-device post-quantum cryptography solutions.The research20 further substantiates these security apprehensions, particularly about reliability and privacy assurance among changing threat models. They argue for blockchain-based solutions to counter man-in-the-middle attacks and illegal data tampering, which are also translatable to edge devices running federated AI models. Their persistent demands for readiness in the quantum age, particularly for latency-sensitive and mission-critical systems, further substantiate our proposed design.The research in21 presented a lightweight hybrid deep learning system specifically aimed at safeguarding user privacy within Federated Computing (FC)-based Industrial Internet of Medical Things (IIoMT). Their research underscores the imperative of reconciling computing economy with cryptographic robustness, hence substantiating our justification for incorporating future-proof, lattice-based encryption into federated learning frameworks utilizing MM1-compatible devices.The literature examined confirms that emergent decentralized AI systems, especially in sensitive and distributed contexts such as digital healthcare, must proactively implement quantum-resilient strategies. These strategies are essential for maintaining long-term data security and system integrity in light of advancing quantum processing capabilities. The architectural decisions presented in our framework are not conjectural but rather a prompt reaction to substantiated system-level issues, as demonstrated in current academic research.Conclusion and future workThis research provides a comprehensive examination of quantum-secure privacy strategies in AI models for handheld devices, concentrating on the incorporation of Fully Homomorphic Encryption (FHE), Differential Privacy (DP), and Federated Learning (FL). This study does not utilize empirical datasets; rather, it concentrates on algorithmic modeling, quantum-resistance evaluation, and hardware feasibility simulation of privacy-preserving components inside the architecture of Apple MM1. Our results demonstrate significant enhancements in user data privacy with the implementation of these sophisticated encryption techniques. FHE and DP exhibit remarkable resilience against reverse engineering and induction attacks, significantly minimizing user data exposure22,23,24. Our testing findings reveal that these advances include intrinsic trade-offs in latency, computational cost, and energy efficiency. Configurations that integrate FHE, DP, and FL demonstrate optimal privacy preservation. However, they incur significant latency and bandwidth costs, indicating that their present form is more appropriate for non-real-time applications or environments with adequate energy resources.Additionally, the scalability evaluation emphasizes the need for more substantial quantum-secure approaches due to the restrictions of a large client base, including increased convergence time and bandwidth usage. DP and FL standalone systems have minimal impact on energy efficiency and processing resources, making them suitable for real-time applications with limited resources. These findings support the development of quantum-resistant AI applications in consumer technology, highlighting the necessity to balance anonymity with performance optimization.Recent advancements in quantum-inspired reinforcement learning and certificateless auditing substantiate our findings. For example25, utilized quantum reinforcement learning to improve the efficiency of relation extraction, illustrating how quantum-enhanced algorithms can uphold both computational efficacy and robustness against noise in dynamic settings—principles that are equally vital in FL-based privacy preservation. Additionally26, introduced a privacy-preserving certificateless auditing mechanism featuring changeable frequency, highlighting customizable privacy levels based on system requirements. Their findings emphasize the essential requirement for flexible, quantum-resistant procedures in dispersed systems, along with our support for hybrid encryption techniques and real-time adaptive anonymization in portable AI devices.This contribution outlines a strategy for improving quantum-resistant AI in handheld settings. Future initiatives must emphasize the creation of lightweight cryptographic algorithms capable of ensuring quantum-secure security while minimizing processing demands27,28,29. Incorporating flexible anonymity methods, such as dynamic encryption that modifies in real-time according to device capacity and network circumstances, is a viable approach. Moreover, investigating hybrid methodologies that integrate conventional and quantum-secure encryption might alleviate resource requirements, so assuring that the shift to quantum-resistant anonymity is feasible within handheld device ecosystems30,31,32. 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Google Scholar Moshawrab, M., Adda, M., Bouzouane, A., Ibrahim, H. & Raad, A. Securing federated learning: Approaches, mechanisms and opportunities. Electronics 13 (18), 3675 (2024).Article Google Scholar Download referencesAcknowledgementsNot applicable.FundingThis work was supported in part by the National Natural Science Foundation of China under Grant 62276091.Author informationAuthors and AffiliationsCollege of Information Science and Engineering, Henan University of Technology, Zhengzhou, Henan, 450001, ChinaNauman Umer, Miaolei Deng & Miao ZhangCollege of Artificial Intelligence and Big Data, Henan University of Technology, Zhengzhou, Henan, 450001, ChinaYuhong ZhangCollege of Water Conservancy and Transportation, Zhengzhou University, Zhengzhou, Henan, 450001, ChinaSheheryar KhanAuthorsNauman UmerView author publicationsSearch author on:PubMed Google ScholarMiaolei DengView author publicationsSearch author on:PubMed Google ScholarYuhong ZhangView author publicationsSearch author on:PubMed Google ScholarMiao ZhangView author publicationsSearch author on:PubMed Google ScholarSheheryar KhanView author publicationsSearch author on:PubMed Google ScholarContributionsAll authors contributed to this research as follows: UN conceptualized this study and led the methodology design; UN and ZY collected and analyzed the data; UN and ZY contributed to data analysis and played a key role in writing the initial draft of the manuscript; UN, ZY, ZM, SK, and DM provided essential support in data visualization and interpretation; DM secured funding for this research project. All authors have read and agreed to the published version of the manuscript.Corresponding authorCorrespondence to Miaolei Deng.Ethics declarations Competing interests The authors declare no competing interests. Additional informationPublisher’s noteSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Rights and permissions Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/. Reprints and permissionsAbout this articleCite this articleUmer, N., Deng, M., Zhang, Y. et al. Quantum resilient security framework for privacy preserving AI in Apple MM1 on device architecture. Sci Rep 15, 38297 (2025). https://doi.org/10.1038/s41598-025-22056-5Download citationReceived: 01 April 2025Accepted: 25 September 2025Published: 03 November 2025Version of record: 03 November 2025DOI: https://doi.org/10.1038/s41598-025-22056-5Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.